At the recent Fashion 2.0 meetup in Manhattan, NY, five startups demoed and presented five minute pitches for innovative ideas designed to help the professionals in the fashion and design industries tackle the challenges they commonly face on a daily basis. The five companies which pitched were: iKwoller, On It’s Own, Entrupy, Showroom Manager and Body Labs.
“Window shopping from the palm of your hand.” This is the promise of Kwoller. Since many merchants provide users with horrible mobile interfaces, lost conversions are the norm.
Kroller aims to solve this issue by combining the entire shopping lifecycle – browsing, social validation and transactions – all into a simple to use interface. This interface happens to be modeled around Tinder, allowing users to swipe left or right to choose if an outfit is fit for them.
With built in price tracking, users are able to save a purchase for when the cost goes down, while machine learning algorithm ensures that every swipe helps to improve the accuracy of suggested designs. If the user isn’t sure the outfit is right for them, social sharing features allow the user to get feedback before they make a purchase.
Despite having a fairly novel concept, Kwoller has a couple of shortcomings worth noting. The biggest concerns raised by the panel at the event revolve around the question of how to keep users hooked on the app. Although the software provides a unique way to shop, when compared to traditional options which can pop up spur of the moment, it can be a challenge keeping users hooked.
The price tracker is a great way to keep value oriented shoppers hooked on the software, but an additional incentive could be to team up with merchants to offer targeted discounts to help move unused inventory, or even target higher priced items to users willing to pay the premium. Ultimately the machine learning algorithm should be leveraged to improve the applications capabilities.
In the fashion world, one of the biggest challenges for designers is ensuring only enough of an outfit is produced to meet demand. Since overstock often is sold below market value, estimating the right amounts of inventory is vital to the success of any business. On Its Own is a startup promising to help designers produce only what is necessary to meet demand.
The most notable feature of this application is the integrated augmented reality capabilities which allow users to try on clothing virtually. Despite having advanced functionality, On Its Own has a fairly simple interface which allows designers to upload their data into pages similar to how Facebook displays information.
From there, prospective customers are shown outfits and they can swipe to decide whether they like the outfit or not. The designers then can view the aggregate data and make inventory decisions based on feedback from the customers.
The concept sounds great in theory, however as the panel mentioned in their critique of the venture, it is one thing for a customer to say they like something Until you actually have them open their wallets and hand the money over, you can’t be sure they’ll actually purchase the good.
The panel also mentioned they have seen similar applications done in the past, and raised the question of how On Its Own will be different than the competition.
One of the biggest competitors to this application is the focus groups retailers already use to predict sales cycles. In the demo, of OnItsOwn there wasn’t really any scientific backing to the way customers are queried. Without knowing the way users are precisely targeted by the software, the future of this app is uncertain considering the fact market research firms already exist to do what this app is doing.